Name
Abstract | ||
|---|---|---|
Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-66471-2_19 | INTERACTIVE COLLABORATIVE ROBOTICS (ICR 2017) |
Keywords | Field | DocType |
Distraction detection, Accident prevention, Convolutional neural networks, Kaggle challenge, Triplet loss | Distraction,Convolutional neural network,Computer science,Speech recognition,Artificial intelligence,Deep learning,Accident prevention,Distracted driving | Conference |
Volume | ISSN | Citations |
10459 | 0302-9743 | 2 |
PageRank | References | Authors |
0.39 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ofonime Dominic Okon | 1 | 2 | 0.39 |
| Li Meng | 2 | 2 | 2.08 |